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yava-code
/
Tessera-1B-Nano-Base

Text Generation
Transformers
Safetensors
llama
next-concept-prediction
conceptlm
causal-lm
smollm2
tessera
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use yava-code/Tessera-1B-Nano-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use yava-code/Tessera-1B-Nano-Base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="yava-code/Tessera-1B-Nano-Base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("yava-code/Tessera-1B-Nano-Base")
    model = AutoModelForCausalLM.from_pretrained("yava-code/Tessera-1B-Nano-Base", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use yava-code/Tessera-1B-Nano-Base with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "yava-code/Tessera-1B-Nano-Base"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "yava-code/Tessera-1B-Nano-Base",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/yava-code/Tessera-1B-Nano-Base
  • SGLang

    How to use yava-code/Tessera-1B-Nano-Base with SGLang:

    Install from pip and serve model
    # Install SGLang from pip:
    pip install sglang
    # Start the SGLang server:
    python3 -m sglang.launch_server \
        --model-path "yava-code/Tessera-1B-Nano-Base" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "yava-code/Tessera-1B-Nano-Base",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker images
    docker run --gpus all \
        --shm-size 32g \
        -p 30000:30000 \
        -v ~/.cache/huggingface:/root/.cache/huggingface \
        --env "HF_TOKEN=<secret>" \
        --ipc=host \
        lmsysorg/sglang:latest \
        python3 -m sglang.launch_server \
            --model-path "yava-code/Tessera-1B-Nano-Base" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "yava-code/Tessera-1B-Nano-Base",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use yava-code/Tessera-1B-Nano-Base with Docker Model Runner:

    docker model run hf.co/yava-code/Tessera-1B-Nano-Base
Tessera-1B-Nano-Base
1.45 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
yava-code's picture
yava-code
Rewrite card intro: lead with the story, move numbers to a section
92732a4 verified 1 day ago
  • .gitattributes
    1.56 kB
    Add the 60-second Space demo video 1 day ago
  • README.md
    3.64 kB
    Rewrite card intro: lead with the story, move numbers to a section 1 day ago
  • config.json
    721 Bytes
    Publish Tessera-1B-Nano-Base checkpoint 4 days ago
  • data_metadata.json
    551 Bytes
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  • demo.mp4
    196 kB
    xet
    Add the 60-second Space demo video 1 day ago
  • eval.json
    252 Bytes
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  • experiment.json
    1.47 kB
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  • generation_config.json
    111 Bytes
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  • merges.txt
    466 kB
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  • metrics.jsonl
    182 kB
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  • model.safetensors
    1.45 GB
    xet
    Publish Tessera-1B-Nano-Base checkpoint 4 days ago
  • special_tokens_map.json
    831 Bytes
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  • tokenizer.json
    3.52 MB
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  • tokenizer_config.json
    3.69 kB
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  • trainer_state.json
    145 Bytes
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  • vocab.json
    801 kB
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